Short-term passenger flow forecast for urban rail transit based on multi-source data

Abstract

Short-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, passenger flow prediction is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic flow prediction model. The model is built using intelligent data provided by a large-scale urban traffic flow warning system, such as accurate passenger flow data, collected using the Internet of things and sensor networks. The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test results on a Beijing traffic dataset show that the SARI-MA–SVM model can improve accuracy and reduce errors in traffic prediction. The obtained pre-diction fits well with the measured data. Therefore, the SARIMA–SVM model can fully charac-terize traffic variations and is suitable for passenger flow prediction.

Introduction

Urban rail transit is the backbone of urban transportation, and its safety management and emergency response efficiency must be improved [1]. It is necessary to further increase the application of the Internet of Things, sensor networks and cloud computing in the field of rail transit safety emergency [2,3,4]. Increasing the application of Internet of things, sensor networks and cloud computing in the field of rail traffic safety emergency, strengthening real-time perception, information sharing and intelligent analysis of passenger flow can effectively improve the dynamic monitoring of rail traffic, intelligent judgment, and the ability to perceive and respond quickly to the scene of emergency [5, 6].

The Internet of things refers to a network of intelligent identification, positioning, tracking, monitoring and management by connecting things with the Internet through information sensing devices [7, 8]. The Internet of Things has the characteristics of comprehensive perception, reliable transmission, and intelligent computing [9, 10]. People combine the existing transportation system with the Internet of Things technology and apply the Internet of Things technology to the construction of intelligent transportation systems. As the related technologies of the Internet of Things are widely used in various fields, the immediacy of information perception and collection technology becomes possible, and the perception ability of the transportation system has been unprecedentedly improved [11,12,13]. Cloud computing is a super computing model, which is a pool of resources [14, 15], but it is not only distributed processing, but also an intelligent processing function which can be managed and coordinated independently on the basis of distributed architecture.

With the application of the Internet of Things, cloud computing and big data technology, the amount of data generated during passenger travel and rail transit operations is showing a rapid growth trend. Comprehensive city perception brings a lot of real-time data. Massive, multi-source, heterogeneous traffic big data and personalized, diversified travel needs pose challenges to passenger flow forecasting methods. This provides a new perspective for scientific research [16,17,18].

With the continuous opening of new lines and the increasing degree of network, the proportion of citizens traveling by urban rail transit is getting higher and higher. At present, most of the urban rail transit operation management practices rely on the experience of dispatchers to judge the current changes in passenger flow. The quantitative passenger flow forecasting methods or systems has not been applied, but the passenger flow forecasted by experience often has a greater error with the actual situation [19, 20]. Therefore, the short-term passenger flow forecast of urban rail transit plays a more important role, which can provide a corresponding basis for the metro operation and dispatching department, and is of great significance for the work of urban rail transit operation management.

The development trend of intelligent system of urban rail transit is to construct large-scale integrated system by using advanced technologies such as computer technology, big data technology, automation technology and Internet of Things technology, which realizes the interconnection of platforms, information interaction and data sharing [21,22,23]. At present, great progress has been made in the integration and application of intelligent system for urban rail transit [24,25,26]. In this paper, a large number of diverse and real data information such as passenger flow data and weather are used to realize the intelligent data aggregation of information resources.

So far, the methods of short-term station passenger flow forecasting for urban rail transit can generally be divided into three categories (linear model, nonlinear model and combined model). The linear model methods include time series model, Kalman filtering model [27,28,29]. The nonlinear model methods include genetic algorithm, neural networks, nonparametric regression model, gray system model, support vector machines, chaos theory, etc. [30,31,32].

The advantage of linear prediction algorithm is that the calculation complexity is low, but the effect is poor when dealing with complex passenger flow data. The nonlinear prediction model can deal with the volatility of passenger flow time series, but it has the shortcomings of complex theory and calculation. The linear model and nonlinear model are not able to fully characterize the short-term urban rail transit traffic, so the combined model has gradually become the focus of research. Based on the ARIMA model and the SVM model to forecast traffic flow, [33] proposes a new approach for traffic flow prediction. [34] addressed two novel neural network structures for short-term railway passenger demand forecasting. [35] proposes a novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction. [36] proposes a hybrid EMD-BPN forecasting approach which combines empirical mode decomposition (EMD) and back-propagation neural networks (BPN).

At present, the short-term traffic passenger flow prediction method based on the combined model can improve the fitting ability of the model to a certain extent, and thus effectively improve the prediction accuracy of the passenger flow.

Generally speaking, the relationship between short-term station passenger flow of urban rail transit and the historical station passenger traffic, large-scale event information and weather conditions is very complicated, and it is not suitable to use a specific linear or nonlinear model to describe. Considering the regularity and time-varying characteristics of urban rail transit passenger flow, based on season autoregressive integrating moving average model (SARIMA) and support vector machine model (SVM), this paper proposes the SARIMA-SVM combination model for urban rail transit short-term station passenger flow forecasting.

This paper proposes the combined model considers the periodic characteristics of passenger flow changes, and considers the nonlinear relationship between short-term passenger flow and passenger flow before and after the period. It makes full use of the existing passenger flow information and realizes the prediction of short-term station passenger flow of urban rail transit.

The rest of this paper is organized as follows. Section 2 discusses the principle of SARIMA model and SVM model, proposes the SARIMA-SVM combination model. The simulation experimental results are shown in Sects. 3, and 4 concludes the paper with summary and future research directions.

Principle of SARIMA-SVM combination model

SARIMA model

The seasonal autoregressive integrated moving average model (SARIMA) [37, 38] is a variant and expansion of the autoregressive integrated moving average model (ARIMA), which fully takes into account the periodicity of the data and is suitable for the daily dynamics of traffic flow. It can not only guarantee the accuracy of the model but also be easily applied to real-time prediction.

The ARIMA model is composed of autoregressive model and moving average model, and is processed by d-order difference. \(ARIMA(p,d,q)\) is expressed by a mathematical formula such as Eq. (1):

$$\phi ({\rm B})\nabla^{d} y(t) = c + \theta (B)\varepsilon (t)$$
(1)

In which, \(y(t)\) and \(\varepsilon (t)\) denote the original time series and the zero-mean white noise sequence, respectively. \(\phi ({\rm B}) = 1 - \phi_{1} B - \phi_{2} B^{2} - \cdots - \phi_{p} B^{p}\), \(\theta (B) = 1 - \theta_{1} B - \theta_{2} B^{2} - \cdots \theta_{q} B^{q}\), \(B\) is a post-shift operator, which satisfies: \(B^{n} y(t) = y(t - n),n = 1,2, \ldots\), \(\nabla^{d} = (1 - B)^{d}\) is processed by d-order difference.

Considering account the periodic characteristics of the time series, the seasonal difference is made to the \(ARIMA(p,d,q)\) model, and the \(SARIMA(p,d,q)(P,D,Q)_{s}\) model is obtained, which is expressed by a mathematical formula such as Eq. (2):

$$\phi (B)\Phi (B^{S} )(1 - B)^{d} (1 - B^{S} )^{D} y(t) = c + \theta (B)\Theta (B^{S} )\varepsilon (t)$$
(2)

In which, \(\Phi ({\rm B}^{S} ) = 1 - \Phi_{1} {\rm B}^{S} - \cdots - \Phi_{p} ({\rm B}^{S} )^{p}\), \(\Theta (B^{S} ) = 1 - \Theta_{1} B^{S} - \cdots \Theta_{q} (B^{S} )^{q}\), \(B^{S}\) denotes the season shift operator, \(B\) denotes the length of the seasonal cycle, \(D\) denotes the order of the seasonal difference, \(P\) denotes the lag order of the seasonal autoregressive term, \(Q\) denotes the lag order of the seasonal moving average term.

SVM model

Support vector machine (SVM) [39, 40] is based on the Vapnik–Chervonenkis dimension theory of statistical learning theory and the principle of structural risk minimization. The architecture of SVM is shown in Fig. 1, where \(K\) is the kernel function:

Fig. 1
figure1

Architecture of SVM

Let \(\{ (x_{i} ,y_{i} ),i = 1,2...,l\}\) be a pair of \(l\) training set samples, where \(x_{i} (x_{i} = [x_{i}^{1} ,x_{i}^{2} , \ldots ,x_{i}^{d} ]^{{\text{T}}} )\) is the input column vector of the \(i\) training set, and \(y_{i}\) is the corresponding output value.

The SVM regression function is: \(f(x) = w\phi (x) + b\), where \(\phi (x)\) denotes a nonlinear mapping function, \(w\) denotes a weight coefficient matrix, \(b\) denotes a threshold. By introducing relaxation variables \(\xi_{i}\), \(\xi_{i}^{*}\) and penalty factors \(C\), and obtain the convex quadratic programming for solving \(w\) and \(b\).

$$\left\{ \begin{aligned} & \min \left[ {\frac{1}{2}\left\| {\left. w \right\|} \right.^{2} + C\sum\limits_{i = 1}^{l} {(\xi_{i} + \xi_{i}^{*} )} } \right] \\ & {\text{subjetct}}\;{\text{to}}\;\left\{ \begin{aligned} & y_{i} - (w\phi (x_{i} ) + b) \le \varepsilon + \xi_{i}^{*} \\ & - y_{i} + w\phi (x_{i} ) + b \le \varepsilon + \xi_{i} \\ & \xi_{i} ,\xi_{i}^{*} \ge 0,\quad i = 1,2, \ldots l \\ \end{aligned} \right. \\ \end{aligned} \right.$$

In which, \(\varepsilon\) denotes the factor of the insensitive loss function, \(\varepsilon\) specifies the error requirement of the regression function; \(C\) is larger, and the penalty for the sample with the training error greater than \(\varepsilon\) is greater.

SARIMA-SVM combination model

The schematic diagram of the SARIMA-SVM combined model in this paper is shown in Fig. 2. The basic idea is: firstly, the passenger flow at the station is forecasted by using the SARIMA model, and the linear variation law of the passenger flow at the station is obtained; secondly, the passenger flow at the station is forecasted by using the SVM model, and the nonlinear variation law of the passenger flow at the station is obtained. The forecasting results of the two models are used as the input of multivariate linear fitting, and the final forecasting results are obtained after fusion.

Fig. 2
figure2

Schematic diagram of SARIMA-SVM predictive model

The mathematical description of the combined prediction model in this paper is shown in Eq. (3).

$$\begin{aligned} Y(f_{1} ,f_{2} ) & = \sum\limits_{i = 1}^{2} {w_{i} (t)f_{it} (t)} + c \\ & =w_{{{\text{SARIMA}}}} (t)f_{{{\text{SARIMA}}}} (t) + w_{{{\text{SVM}}}} (t)f_{{{\text{SVM}}}} (t) + c \\ \end{aligned}$$
(3)

In which, \(Y(f_{1} ,f_{2} )\) denotes the station passenger flow data at a certain time; \(f_{it} (t)\) denotes the predicted value of the i-th prediction method at time t; \(w_{i} (t)\) denotes the weight of the i-th prediction method; C is a constant.

Model application

Basic data

The study uses the passenger flow data from May 4, 2015 (Monday), to June 14, 2015 (Sunday), avoiding major holidays and similar events, with a major impact on urban traffic passenger flow.

To verify the practicability of the model, for different types of subway stations, the 15-min inbound statistics were selected for three stations: Beijing Taoranting Station (Ordinary Station), BeijingNan Station (Pivot Station) and Gongzhufen Station (Transfer Station). The study uses data on 6 weeks, 7 days a week, the sampling interval is 15 min, and the sampling time is 06:00 to 22:45 every day. The inbound traffic data of Taoranting Station, BeijingNan Railway Station and Gongzhufen Station are shown in Figs. 3, 4 and 5. Data on the first 5 weeks of passenger flow are used as the training set, data on the last week are used as the test set.

Fig. 3
figure3

Passenger trends of TaoRanTing station

Fig. 4
figure4

Passenger trends of BeiJingNan station

Fig. 5
figure5

Passenger trends of GongZhuFen station

As we all know, urban rail transit passenger flow has obvious differences between working days and weekends. It can also be seen from the inbound passenger flow trend graph that the difference between weekday passenger flow and weekend passenger flow is more obvious. Therefore, this paper divides passenger flow into two types, workday and weekend.

SARIMA model

Rail transit passenger flow has a significant feature of daily periodicity, that is, there is a certain commonality at the corresponding time every day.

In order to predict the passenger flow \(\hat{V}_{i}^{{}} (t)\) of the station at the t-th day of the i-th day (the measured value is \(V_{i}^{{}} (t)\)), the time series formed by the latest k-time passenger flow recorded by the station before the predicted time t is the relevant time of \(\hat{V}_{i}^{{}} (t)\). The time series is shown in Eq. (4).

$$\hat{V}_{i} (t) = \{ V_{i}^{{}} (t - l),1 \le l \le k\}$$
(4)

Step 1 Verify the stability of the passenger flow data;

Step 2 Set the initial values of the p, d, q, P, D, Q, and S parameters;

Step 3 Estimation \({\text{SARIMA}}(p,d,q)(P,D,Q)_{s}\) model;

Step 4 Estimate the obtained \({\text{SARIMA}}(p,d,q)(P,D,Q)_{s}\) model and test the analysis to verify the residual of the fitted model to confirm that the model can adequately describe the data;

Step 5 Select the optimal SARIMA model setting based on the corresponding Akaike information criterion (AIC) value or Schwarz information criterion (SIC) value.

Since the daily periodicity of the sequence is to be considered in the prediction model, the sampling interval is 15 min, the sampling time is 6:00 to 22:45 every day, the daily passenger flow data is 68, and the model parameter is S = 68.

Through the SPSS parameter estimation, the parameters of the optimal SARIMA model for the corresponding three station traffic flows are shown in Table 1.

Table 1 Parameter of SARIMA

SVM prediction model

The passenger flow is a cyclical change of 7 days. Assume that the time period of short-term forecast is T (15 min in this paper), 1 day can be divided into multiple observation periods, and the traffic volume of a certain observation period is closely related to the previous s observation period.

The passenger flow forecast for a certain observation period of a certain d-day t-period is obtained based on the passenger flow data of the previous m-week of the day, the n-days before the week, and the s-period of the day before, as shown in Eq. (5).

$$V_{d}^{t} = f(V_{d - 7m}^{t} , \ldots ,V_{d - 14}^{t} ,V_{d - 7}^{t} ;V_{d - n}^{t} , \ldots ,V_{d - 2}^{t} ,V_{d - 1}^{t} ;V_{d}^{t - s} , \ldots ,V_{d}^{t - 2} ,V_{d}^{t - 1} )$$
(5)

In the Matlab platform, the LIBSVM toolbox is called by programming, the kernel function is RBF kernel function, and the 50-fold cross-validation is set. The genetic algorithm determines the optimal parameters of the SVM model, and uses the trained SVM model to predict. The model parameters are shown in Table 2.

Table 2 Parameter of SVM

SARIMA-SVM combined model

According to the previous analysis, the SARIMA and SVM model prediction results reflect the real passenger flow, and the multiple linear regression analysis is performed by SPSS to obtain the regression coefficient. The results are shown in Table 3. The comparison of inbound passenger traffic forecasts is shown in Figs. 6, 7, and 8.

Table 3 Multiple regression coefficient
Fig. 6
figure6

Comparison chart of TaoRanTing passenger prediction

Fig. 7
figure7

Comparison chart of BeiJingNan passenger prediction

Fig. 8
figure8

Comparison chart of GongZhuFen passenger prediction

Results and analysis

In this study, the performance of the model was evaluated by root-mean-square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), see Eqs. (6), (7), and (8). The RMSE is suitable for comparison between different models of the same data set, and the MAPE can be used for comparison between different data sets. The RMSE can be used as the main evaluation index of model robustness.

$${\text{RMSE}} = \sqrt {\frac{1}{n}\sum\limits_{i = 1}^{n} {(y_{t} - \hat{y}_{t} )^{2} } }$$
(6)
$${\text{MAE}} = \frac{1}{n}\sum\limits_{i = 1}^{n} {\left| {y_{t} - \hat{y}_{t} } \right|}$$
(7)
$${\text{MAPE}} = \left( {\frac{1}{n}\sum\limits_{i = 1}^{n} {\left| {\frac{{(y_{t} - \hat{y}_{t} )}}{{y_{t} }}} \right|} } \right) \times 100\%$$
(8)

In which, \(y_{t}\) indicates the actual observed value of the passenger flow, \(\hat{y}_{t}\) indicates the predicted value of the passenger flow, and n is the predicted sample. The model performance evaluation pairs are shown in Table 4.

Table 4 Performance evaluation comparison of model

It can be seen from Table 4 that the SARIMA-SVM model error is significantly reduced compared to SARIMA and SVM for Taoranting Station and Gongzhufen Station. For BeijingNan Railway Station, SARIMA-SVM model is more robust than SARIMA and SVM, and the error is significantly lower than SARIMA.

Conclusion

Short-term passenger flow forecasting is essential for the operation and management of rail transit. However, the change of passenger flow in urban rail transit stations is characterized by complexity, nonlinearity and periodicity. Thus, a single forecasting method cannot fully describe the changing patterns of passenger flow and is not applicable to, daily passenger flow forecasting. Based on the Internet of Things technology and Sensor networks, aiming at the characteristics of the change of passenger flow in Beijing rail transit, this paper proposes a SARIMA-SVM passenger flow combination forecasting model, realizes the accurate judgment and intelligent analysis of the large passenger flow. The test results show that the model effectively improves the accuracy of passenger traffic prediction, reduces the prediction error, and can well describe the variation law of passenger flow. It has broad application prospects in urban rail transit short-term passenger flow prediction.

In order to deeply study the problem of urban rail transit passenger flow prediction, this paper proposes a rail transit passenger flow prediction model based on the SARIMA-SVM, which makes up for the deficiencies of previous studies. Compared with the existing forecasting method, this method is closer to the actual situation and provides strong support for the accurate forecast of passenger flow. This model provides a theoretical basis for the government and related departments to formulate traffic management measures. How to further update the data in the model to obtain more accurate results, study the daily changes of passenger behavior after short-term incidents, and develop passenger flow organization methods when short-term incidents occur in subway stations are the directions of future research.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

SARIMA:

Season autoregressive integrating moving average

SVM:

Support vector machine

RMSE:

Root-mean-square error

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

References

  1. 1.

    M. Zhou, H. Dong, B. Ning, F. Wang, Parallel urban rail transit stations for passenger emergency management. IEEE Intell. Syst. (2019). https://doi.org/10.1109/MIS.2019.2963192

    Article  Google Scholar 

  2. 2.

    X. Yu, F. Sun, X. Cheng, Intelligent urban traffic management system based on cloud computing and Internet of Things, in 2012 International Conference on Computer Science and Service System (2012), pp. 2169–2172

  3. 3.

    C. Luo, Y. Song, Subway security monitoring based on Internet of Things. J. Eng. Manag. 27, 35 (2013)

    Google Scholar 

  4. 4.

    J.S.O. Neto, S.T. Kofuji, Inclusive smart city: expanding design possibilities for persons with disabilities in the urban space, in 2016 IEEE International Symposium on Consumer Electronics (ISCE) (2016), pp. 59–60

  5. 5.

    H. Zheng, W. Guo, N. Xiong, A kernel-based compressive sensing approach for mobile data gathering in wireless sensor network systems. IEEE Trans. Syst. Man Cybern. Syst. 48, 2315 (2018)

    Article  Google Scholar 

  6. 6.

    Z. Huang, X. Xu, J. Ni, H. Zhu, C. Wang, Multimodal representation learning for recommendation in Internet of Things. IEEE Internet Things J. 6, 10675 (2019)

    Article  Google Scholar 

  7. 7.

    Z. Chen, F. Xia, T. Huang, F. Bu, H. Wang, A localization method for the Internet of Things. J. Supercomput. 63, 657 (2013)

    Article  Google Scholar 

  8. 8.

    G. Broll, E. Rukzio, M. Paolucci, M. Wagner, A. Schmidt, H. Hussmann, PERCI: pervasive service interaction with the Internet of Things. IEEE Internet Comput. 13, 74 (2009)

    Article  Google Scholar 

  9. 9.

    Z. Ju, Y. Li, Analysis on Internet of Things (IOT) based on the “subway supermarket” e-commerce mode of TESCO, in 2011 International Conference on Information Management, Innovation Management and Industrial Engineering, vol. 2 (2011), pp. 430–433

  10. 10.

    Y. Zhang, R. Zhu, Z. Chen, J. Gao, D. Xia, Evaluating and Selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data. Eur. J. Oper. Res. (2020). https://doi.org/10.1016/j.ejor.2020.09.028

    Article  Google Scholar 

  11. 11.

    H. Liang, J. Zou, Z. Li, M.J. Khan, Y. Lu, Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm. Future Gener. Comput. Syst. 95, 454 (2019)

    Article  Google Scholar 

  12. 12.

    H. Liang, A. Xian, M. Mao, P. Ni, H. Wu, A research on remote fracturing monitoring and decision-making method supporting smart city. Sustain. Cities Soc. 62, 102414 (2020)

    Article  Google Scholar 

  13. 13.

    Q. Zhang, C. Zhou, N. Xiong, Y. Qin, X. Li, S. Huang, Multimodel-based incident prediction and risk assessment in dynamic cybersecurity protection for industrial control systems. IEEE Trans. Syst. Man Cybern. Syst. 46, 1429 (2016)

    Article  Google Scholar 

  14. 14.

    Y. Zhou, D. Zhang, N. Xiong, Post-cloud computing paradigms: a survey and comparison. Tsinghua Sci. Technol. 22, 714 (2017)

    Article  Google Scholar 

  15. 15.

    V.L. Tran, A. Islam, J. Kharel, S.Y. Shin, On the application of social Internet of Things with fog computing: a new paradigm for traffic information sharing system, in 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud) (2018), pp. 349–354.

  16. 16.

    Y. Wang, Z. Lv, L. Huang, Urban traffic state forecast based on Internet of Things sensors. J. Wuhan Univ. Technol. 32, 108 (2010)

    Google Scholar 

  17. 17.

    K. Huang, Q. Zhang, C. Zhou, N. Xiong, Y. Qin, An efficient intrusion detection approach for visual sensor networks based on traffic pattern learning. IEEE Trans. Syst. Man Cybern. Syst. 47, 2704 (2017)

    Article  Google Scholar 

  18. 18.

    H. Liang, J. Zou, K. Zuo, M.J. Khan, An improved genetic algorithm optimization fuzzy controller applied to the wellhead back pressure control system. Mech. Syst. Signal Process. 142, 106708 (2020)

    Article  Google Scholar 

  19. 19.

    M. Zhou, H. Dong, P.A. Ioannou, Y. Zhao, F. Wang, Guided crowd evacuation: approaches and challenges. IEEE/CAA J. Automat. Sin. 6, 1081 (2019)

    Article  Google Scholar 

  20. 20.

    M. Zhou, H. Dong, Y. Zhao, P.A. Ioannou, F. Wang, Optimization of crowd evacuation with leaders in urban rail transit stations. IEEE Trans. Intell. Transp. Syst. 20, 4476 (2019)

    Article  Google Scholar 

  21. 21.

    Z. Huang, G. Shan, J. Cheng, J. Sun, TRec: an efficient recommendation system for hunting passengers with deep neural networks. Neural Comput. Appl. 31, 209 (2019)

    Article  Google Scholar 

  22. 22.

    B. Wu, X. Yan, Y. Wang, C.G. Soares, An evidential reasoning-based CREAM to human reliability analysis in maritime accident process. Risk Anal. 37, 1936 (2017)

    Article  Google Scholar 

  23. 23.

    B. Wu, L. Zong, X. Yan, C.G. Soares, Incorporating evidential reasoning and TOPSIS into group decision-making under uncertainty for handling ship without command. Ocean Eng. 164, 590 (2018)

    Article  Google Scholar 

  24. 24.

    K. Yu, H. Zhu, H. Cao, B. Zhang, E. Chen, J. Tian, J. Rao, Learning to detect the subway station arrival for mobile users, in Proceedings of the 14th Intelligent Data Engineering and Automated Learning (n.d.), pp. 49–57

  25. 25.

    P.D. Yoo, A.Y. Zomaya, Combining analytic kernel models for energy-efficient data modeling and classification. J. Supercomput. 63, 790 (2013)

    Article  Google Scholar 

  26. 26.

    C. Gosman, C. Dobre, F. Pop, Privacy-preserving data aggregation in intelligent transportation systems, in 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) (2017), pp. 1059–1064

  27. 27.

    L. Lin, Q. Shu, Research on method of short-term traffic flow prediction of highway. Comput. Simul. 34, 123 (2017)

    Google Scholar 

  28. 28.

    K. Kumar, V.K. Jain, Autoregressive integrated moving averages (ARIMA) modelling of a traffic noise time series. Appl. Acoust. 58, 283 (1999)

    Article  Google Scholar 

  29. 29.

    B.L. Smith, B.M. Williams, R.K. Oswald, Comparison of parametric and nonparametric models for traffic flow forecasting. Transp. Res. Part C Emerg. Technol. 10, 303 (2002)

    Article  Google Scholar 

  30. 30.

    X. Chen, X. Liu, Z. Wei, J. Liang, Y. Cai, L. Chen, Short-term traffic flow forecasting of road network based on GA-LSSVR model. J. Transp. Syst. Eng. Inf. Technol. 17, 60 (2017)

    Google Scholar 

  31. 31.

    D. Hu, J. Xiao, C. Che, Lifting wavelet support vector machine for traffic flow prediction. Appl. Res. Comput. 24, 275 (2007)

    Google Scholar 

  32. 32.

    B.L. Smith, M.J. Demetsky, Short-term traffic flow prediction: neural network approach. Transp. Res. Rec. 1453, 98 (1994)

    Google Scholar 

  33. 33.

    M. Tan, Y. Li, J. Xu, A hybrid ARIMA and SVM model for traffic flow prediction based on wavelet denoising. J. Highw. Transp. Res. Dev. 26, 127 (2009)

    Google Scholar 

  34. 34.

    T.-H. Tsai, C.-K. Lee, C.-H. Wei, Neural network based temporal feature models for short-term railway passenger demand forecasting. Expert Syst. Appl. 36, 3728 (2009)

    Article  Google Scholar 

  35. 35.

    W. Deng, W. Li, X. Yang, A novel hybrid optimization algorithm of computational intelligence techniques for highway passenger volume prediction. Expert Syst. Appl. 38, 4198 (2011)

    Article  Google Scholar 

  36. 36.

    W. Yu, M. Chen, Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp. Res. Part C Emerg. Technol. 21, 148 (2012)

    Article  Google Scholar 

  37. 37.

    G.E.P. Box, G. Jenkins, Time Series Analysis, Forecasting and Control (Holden-Day Inc, USA, 1990).

    Google Scholar 

  38. 38.

    K.-Y. Chen, C.-H. Wang, A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan. Expert Syst. Appl. 32, 254 (2007)

    Article  Google Scholar 

  39. 39.

    G.F. Smits, E.M. Jordaan, Improved SVM regression using mixtures of kernels, in Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No.02CH37290), vol. 3 (2002), pp. 2785–2790

  40. 40.

    C.J. Hsieh, K.W. Chang, C.J. Lin, S.S. Keerthi, S. Sundararajan, A dual coordinate descent method for large-scale linear SVM, in ICML 2008 (2008)

Download references

Funding

This work was supported in part by the Fundamental Research Funds for the Central Universities (Grant No. 2018JBZ002), in part by the National Natural Science Foundation of China (Grant No. U1834211), and in part by the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2020ZZ002).

Author information

Affiliations

Authors

Contributions

WL and MZ contributed to the conception of the study, performed the experiment and performed the data analyses and wrote the manuscript. LS and HD helped perform the analysis with constructive discussions.

Corresponding author

Correspondence to Min Zhou.

Ethics declarations

Research involving human participants and/or animals

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

All authors agree to submit this version and claim that no part of this manuscript has been published or submitted elsewhere.

Competing interests

The author declares that he has no competing interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, W., Sui, L., Zhou, M. et al. Short-term passenger flow forecast for urban rail transit based on multi-source data. J Wireless Com Network 2021, 9 (2021). https://doi.org/10.1186/s13638-020-01881-4

Download citation

Keywords

  • Internet of things
  • Intelligent data aggregation
  • Urban traffic
  • Season autoregressive integrating moving average
  • Support vector machine